Image processing systems are utilized for capturing images of documents. Such images may be stored if not used immediately for processing of information or immediately displayed to an operator via a terminal at a workstation. The quality of the images captured by an image processing system is related to the number of bits per picture element which are chosen to represent portions of the image. Generally, an image processing system captures an image, digitizes the captured image for storage and displays the image. Image processing systems also utilize filters for enhancing the image which results in a sharper and more definite display of the original image. Filtering may be utilized to remove noise from the digitized signal or to compensate for optical parameters in the image capture equipment. Images with low print contrast often suffer degradation from finite precision arithmetic performed by the filter.
In image processing systems, it is also desirable to reduce the representation of the image to a smaller number of bits per picture element without an appreciable loss of information to accomplish very high speed processing of images. Unlike image compression which requires a reduction in the amount of stored data, high speed processing requires that the image have a small number of bits per picture element. It is therefore desirable to reduce the number of bits per picture element for both image reduction for subsequent processing and to solve finite arithmetic processing problems.
The reduction of the number of bits per picture element which represent an image can be accomplished by thresholding the image. Another technique used is to scale the image by a constant so that the dynamic range of the input image matches the dynamic range of the desired image. For example, the input image may have four bits per picture element and the desired image has two bits per picture element. Adaptive quantizing methods have also been proposed, but merely address the digitization of an original analog signal. By filtering and scaling the image before thresholding, the resulting image can also be improved; however, these results are not completely satisfactory.
The major source of degradation of the desired image is the finite precision arithmetic noise introduced into the signal from the finite arithmetic steps performed by the digital filter. Presently, two methods of implementing finite precision arithmetic are used. These methods either round or truncate the results of the arithmetic operations in order to maintain the same number of bits per sample in the output data as the input data. With the use of accumulators and multiply-accumulators having extra precision bits, it is possible to delay rounding or truncating the result until all the arithmetic operations are completed. However, the noise is only reduced and not eliminated.
A need has thus arisen for a signal processing system such as used in an image processing system in which finite precision arithmetic is performed with a minimum amount of finite precision arithmetic noise. A need has further arisen for an image processing system which allows for the number of bits per picture element to increase while filtering and which adaptively quantizes the resulting image to a reduced desired number of bits per picture element.